TLS

Code for automatic lung and airway segmentation and evaluation. Visit Project Drive!

Segmentation Modules

Pedro's new filters

AirwaySegmentationAuto_v2: Macaques version.
Lungs_Segmentation: Include Hu, labeleize and morpholical filter for macaques lung segmentation.
SpeedImageCalc: Calculation of Hessian features
Checkers: Additional functions
Externals: ImageJ/ITK Filters
Libs: Dicom utilities
Propagator: Fast Marching algorithm for airway segmentation
Registration: Registration filters
Utilities: Help's filters used by the rest of projects

Utilities

Waterfalls code.

Java

Batch mode scripts.
Similitude evaluation
ImageJ plugings to open MMWKS images(.hdr, .img) and ITK files(.mhd,.raw).

Python

Batch mode filters.
Manual thresholding tissue classification
Help's functions

Clasification

Manual and automatic

Compiling

In order to complile the code sucessfully the following packages must be installed before:

g++/gcc (Compiler)
git-core
git-svan
libfontconfig-dev
libgl1-mesa-dev
libglu1-mesa-dev
libncurses5-dev
libosmesa6-dev
libx11-dev
libxrender-dev
libxt-dev
make
python-dev
python-numpy
subversion
libbz2-dev

The libraries set could be installed in one step by running the following line:

sudo apt-get install g++ gcc git-core git-svn libfontconfig-dev libgl1-mesa-dev libglu1-mesa-dev libncurses5-dev libosmesa6-dev libx11-dev libxrender-dev libxt-dev make python-dev python-numpy subversion libbz2-dev

In this version is necessary to perform the compilation in two phases. A first compilation to install the dependecies CMake TLS_SECOND_STEP (compilation option unchecked) and second one with CMake TLS_SECOND_STEP checked.

References

@article{Gordaliza2018,
doi = {10.1038/s41598-018-28100-x},
issn = {2045-2322},
journal = {Sci. Rep.},
keywords = {Biomedical engineering,Computer science,Experimental models of disease,Infectious,disease epidemiology},
month = {dec},
number = {1},
publisher = {Nature Publishing Group},
title = {{Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model}},
url = {http://www.nature.com/articles/s41598-018-28100-x},
volume = {8},
year = {2018}
}